Non-destructive testing, in particular for pipes during manufacture or in the finished state

ABSTRACT

Device forming an operating tool, for the non-destructive testing of iron and steel products, intended to extract information on possible imperfections in the product, from feedback signals that are captured by transmitting ultrasound sensors, receiving ultrasound sensors forming an arrangement with a selected geometry, assembled to couple in an ultrasound way with the product via the intermediary of a liquid medium, with relative rotation/translation movement between the pipe and the arrangement of transducers, said operating tool being characterized in that it comprises: a converter ( 891; 892 ) capable of selectively isolating a digital representation of possible echoes in designated time windows, as a function of the relative rotation/translation movement, said representation comprising the amplitude and time of flight of at least one echo, and of generating a parallelepipedic 3D graph, a transformer unit ( 930 ) capable of generating a 3D image ( 901; 902 ) of possible imperfections in the pipe from the 3D graph and a database, a filter ( 921; 922 ) capable of determining, in the images ( 901; 902 ), presumed imperfection zones (Zcur), and the properties of each presumed imperfection, and an output stage configured to generate a product conformity or non-conformity signal.

The invention concerns the non-destructive testing of materials,especially for pipes in the process of manufacture.

Various options, more of which later, are known which tend to use neuralnetworks in connection with non-destructive testing of materials. Butthose currently in existence are unable to operate in an industrialenvironment, on equipment already in service, in real time, whilstallowing a classification on the fly of imperfections according to theirtype, in such a way that it is possible to quickly remedy a problem,arising during the production phase.

The unpublished French patent application No. 0605923 deals withnon-destructive testing.

An object of the invention is to improve the situation by moving towardsa system that:

-   -   can be used in an industrial environment and can be easily        installed on equipment that already exists in this environment;    -   can be used in real time, that is to say can provide rapid        diagnosis, in particular at a speed that is fast enough not to        slow down the overall speed of production, and    -   allows a classification of imperfections according to their        type, on the basis of a small amount of information, in order to        know their severity and allow a determination of the technical        reason for the imperfection, as well as the rapid remedying of        the problem during the production phase.

According to an initial aspect of the invention, a device is proposedthat forms an operating tool for the non-destructive testing of pipes(or other iron and steel products) during and at the end of production.Such a tool is intended to extract information on possible imperfectionsin the product. Transmitting ultrasound sensors are excited selectivelyaccording to a selected time rule. Feedback signals are captured byreceiving ultrasound sensors forming an arrangement with a selectedgeometry, mounted in ultrasound coupling with the pipe via theintermediary of a liquid medium. Finally, there is generally a relativerotation/translation movement between the product and the transducerarrangement.

The operating tool proposed comprises:

-   -   a converter, capable of selectively isolating a digital        representation of possible echoes in designated time windows, as        a function of the relative rotation/translation movement, and        extracting from this an image of possible imperfections in the        product, which representation includes the amplitude and the        time of flight of at least one echo, and of generating a 3D        parallelepipedic graph;    -   a transformer unit capable of generating a 3D image of possible        imperfections in the pipe on the basis of the 3D graph and a        database;    -   a filter, capable of determining, in the images, presumed        imperfection zones, as well as the properties of each presumed        imperfection;    -   an output stage configured to generate a product conformity or        non-conformity signal.

The invention is equally at home a non-destructive testing device forpipes (or other iron and steel products) during or at the end ofproduction, which comprises:

-   -   an arrangement of ultrasound transducers with a selected        geometry, mounted in ultrasound coupling with the pipe via the        intermediary of a coupling medium, with relative        rotation/translation movement between the pipe and the        transducer arrangement;    -   circuits to selectively excite these transducer elements        according to a selected time rule, and for gathering the        feedback signals they capture, and    -   an operating tool as defined above.

Another aspect of the invention manifests itself in the form of anon-destructive testing procedure for pipes (or other iron and steelproducts) during or at the end of production, comprising the followingstages:

-   a. providing an arrangement of ultrasound transducers with a    selected geometry, mounted in ultrasound coupling with the pipe via    the intermediary of a coupling medium, with relative    rotation/translation movement between the pipe and the transducer    arrangement;-   b. selectively exciting these transducer elements according to a    selected time rule;-   c. gathering the feedback signals they capture, in order to    selectively analyse these feedback signals, so as to extract    information on any imperfections in the pipe, said information    including the amplitude and the time of flight of at least one echo,    and generating a 3D parallelepipedic graph;-   d. selectively isolating a digital representation of possible echoes    in designated time windows, as a function of the relative    rotation/translation movement, and extracting from this a 3D image    of possible imperfections in the pipe on the basis of the 3D    parallelepipedic graph and a database;-   e. generating a product conformity or non-conformity signal.

Step e may comprise:

-   -   e1. filtering the images according to selected filter criteria,        in order to determine presumed imperfection zones (Zcur) there,        and the properties of each presumed imperfection;    -   e2. forming working digital inputs, from an extract of the        images corresponding to a presumed imperfection zone (Zcur),        properties of the presumed imperfection in the same zone, coming        from the filter, and contextual data;    -   e3. applying the inputs so formed to at least one arrangement of        the neural circuit type;    -   e4. digitally processing the output from the arrangement of the        neural circuit type according to selected decision criteria, in        order to draw from this a decision and/or an alarm, and    -   e5. separating and marking pipes considered not to conform by        stage e4.

Other aspects, characteristics and advantages of the invention willbecome apparent upon examination of the detailed description thatfollows of various non-restrictive embodiments and the attacheddrawings, in which:

FIG. 1 is a schematic perspective view of a pipe with imperfections ordefects so-called reference imperfections or reference defects;

FIG. 2 is a schematic side view illustrating an example of aninstallation of the “rotating head testing” type on a pipe at the end ofproduction;

FIGS. 3A to 3C are details of various types of thickness measurement andlongitudinal and transverse imperfection testing;

FIG. 4 is the schematic view of the electronics associated with anultrasound sensor in non-destructive testing in a conventionalinstallation;

FIGS. 5A and 5B are an end view and a side view of a particular type ofnon-destructive testing cell, commonly known as a “rotating head” andshown schematically;

FIG. 6 shows the complexity of the ultrasound trajectories encounteredin a pipe, in a simple example;

FIGS. 6A and 6B are schematic timing diagrams of ultrasound signals, fora sensor under oblique incidence and for a sensor under normal(perpendicular) incidence, respectively;

FIG. 7 is a graph showing a conventional representation of theselectivity of a testing installation;

FIG. 8 is a schematic view of the electronics associated with anultrasound sensor in non-destructive testing in an example of aninstallation capable of implementing the invention;

FIG. 8A is a more detailed block diagram of part of FIG. 8;

FIG. 8B is another more detailed block diagram of part of FIG. 8;

FIG. 9 is a schematised screen shot showing two digitised ultrasoundimages of potential imperfections in a pipe;

FIG. 9A is a screen shot from a different angle;

FIGS. 10A to 10D are schematic representations of various types ofimperfections according to the American Petroleum Institute (API)classification and which constitute the output data from the neuralnetwork tending to determine the type of imperfection;

FIG. 11 is a more detailed block diagram of another part of FIG. 8;

FIG. 11A is a detailed view of the transformer unit of FIG. 11;

FIG. 12 is a sequence chart illustrating the processing of successivepotential imperfections in an image;

FIG. 13 is a block diagram of a system of filters;

FIG. 14 is a block diagram of a neural network setup tending todetermine the type of imperfection in a pipe;

FIG. 15 is a block diagram of a neural network setup tending todetermine the degree of severity of an imperfection in a pipe;

FIG. 16 is a block diagram of the neuron model;

FIG. 17 is an example of an elementary neuron transfer function; and

FIG. 18 is the general diagram of an installation for the detection ofdefects using various types of sensors.

The drawings contain elements of a definite nature. They can thereforenot only serve to better understand the present invention but can alsocontribute to its definition, as necessary.

In the remainder of this text, an ultrasound sensor may be referred towithout distinction as a sensor, or probe or transducer, all of whichare well-known to a person skilled in the art.

Neural Networks

The use of neural networks in connection with non-destructive testing ofmaterials has been the subject of numerous publications, mostly quitetheoretical, which will be considered now.

The article entitled ‘Localization and Shape Classification of Defectsusing the Finite Element Method and the Neural Networks’ by ZAOUI,MARCHAND and RAZEK (NDT.NET—AUGUST 1999, Vol. IV, abridged Number 8)formulates proposals in this area. However, these proposals are made inthe context of activities in the laboratory, and the applicationdescribed does not allow implementation in the production line of anindustrial environment. Furthermore, only the detection by Eddy currentsis dealt with, which is often inadequate.

The article entitled ‘Automatic Detection of Defects in IndustrialUltrasound Images using a Neural Network’ by Lawson and Parker (Proc. ofInt. Symposium on Lasers, Optics, and Vision for Productivity inManufacturing I (Vision Systems: Applications), June 1996, Proc. of SPIEvol. 2786, pages 37-47 1996), describes the application of imageprocessing and neural networks to the so-called scan TOFDinterpretation. The TOFD (Time of Flight Diffraction) method consists ofpinpointing the positions of the ultrasound sensor where it is possibleto observe a diffraction of the beam at the edges of the imperfection,which allows subsequent dimensioning of the imperfection. This method isdifficult to adapt to existing non-destructive testing equipment,particularly in an industrial environment.

The article entitled ‘Shape Classification of Flaw Indications in3-Dimensional Ultrasonic Images’ by Dunlop and McNab (IEEProceedings—Science, Measurement and Technology—July 1995—Volume 142,Issue 4, pages 307-312) concerns diagnostics in relation to pipelinecorrosion. The system allows in-depth non-destructive testing and allowsa three-dimensional study in real time. However, the system is veryslow. This makes its use in an industrial environment relativelydifficult.

The article entitled ‘Application of neuro-fuzzy techniques in oilpipelines ultrasonic non-destructive testing’ by Ravanbod (NDT&EInternational 38 (2005), pages 643-653) suggests that the imperfectiondetection algorithms can be improved by the use of fuzzy logic elements,in combination with the neural network. Here again, however, thetechniques studied concern the inspection of pipeline imperfections anddiagnosis of corrosion imperfections.

DE 42 01 502 C2 describes a method for creating a signal intended for aneural network but provides little or no information on theinterpretation of the results, in diagnostics terms. Furthermore, onceagain, only detection by Eddy currents is dealt with.

Japanese patent publication 11-002626 concerns the detection oflongitudinal imperfections only, and solely by Eddy currents.

Patent publication No. 08-110323 limits itself to a study of thefrequency of the signals obtained by ultrasound.

Patent publication No. 2003-279550 describes a program fordifferentiating between a zone qualified as good and a bad zone of aproduct using a neural network. This program goes no further, and allowsneither the classification nor the localisation of imperfections. As aconsequence, the application of this program may frequently lead to therejection of parts that would be deemed good if the results had beeninterpreted by a human operator.

Non-Destructive Testing of Pipes

The following detailed description is provided essentially in thecontext of non-destructive testing of pipes as they leave production,but without this being restrictive.

As indicated in FIG. 1, the imperfections in a pipe T can be identifiedaccording to their position. So, surface imperfections, internal orexternal, include longitudinal imperfections LD, and circumferential (ortransverse or crosswise or transversal) imperfections CD and oblique orinclined imperfections ID; by various arrangements of sensors, anattempt is made to detect these as soon as they extend beyond a lengthand a depth defined according to the standards or specifications orcustomer requirements (for example, an imperfection length valuementioned in the standards is ½ inch, or approximately 12.7 mm, with adepth of approximately 5% of the thickness of the product tested).Imperfections meeting these criteria are called defects. Imperfections“in the wall” are also of interest, that is to say in the mass MD (notvisible in FIG. 1), which often correspond to inclusions and split ends,the detection of which is attempted at the same time as the thicknessmeasurement. The ultrasound beams are shown diverging in FIG. 1 in orderto explain the detection of imperfections. In practice they will bequite convergent, as will be seen.

Conventionally, in non-destructive testing by ultrasounds, one of thefollowing three types of installations is used: so-called ‘rotatinghead’ installations, so-called ‘rotating pipe’ installations, andmulti-element encircling sensor installations, all of which arewell-known to a person skilled in the art. In the case of the use ofsensors that operate by electronic scanning, the relative pipe/sensorsrotation is virtual. When used here, the expression ‘relativerotation/translation movement between the pipe and the transducerarrangement’ covers the case where the relative rotation is virtual.

In FIG. 2, the rotating head non-destructive testing machine comprisesan ultrasound device, properly so-called, mounted on a water enclosure,or water box 100, which crosses the pipe T at a speed of v=0.5 metersper second, for example. The ultrasound sensors or probes emitlongitudinal waves in the water. A given sensor works, for example, at 1or a few MHz. It is excited, repeatedly, by pulses of a selectedwaveform, at a rate (or frequency) of recurrence Fr, also known as pulserepetition frequency (PRF), which is of the order of a few kHz or tensof kHz, for example 10 kHz.

Moreover, an ultrasound transducer has:

-   -   a near-field radiation, practically parallel, in a so-called        Fresnel zone, home to numerous interferences, whose length along        the axis of the beam is        N=0.25D ²/λ        where D is the diameter of the active pad of the transducer, and        λ its working wavelength, and    -   a far-field radiation, in the so-called Fraunhofer zone,        according to a divergent beam of angle 2α, with        sin α=1.22λ/D

FIGS. 3A, 3B and 3C represent sensors made to converge by means of aconcave (ultrasound) lens, as currently used in pipe testingapplications. The Fraunhofer zone is preferably used as there is lessdisturbance there.

So, for sensors such as P11 and P12, the ultrasound beam, which isgenerally in focus, extends to the vicinity of a plane perpendicular tothe axis of the pipe T. Detection is therefore carried out noticeably incross-section. Their roles are as follows:

-   -   either their beam is also perpendicular to the axis of the pipe        T in the cross-section, and they serve to measure the thickness        (for example P1, FIG. 3A); this is then referred to as “straight        probing”.;    -   or their beam has an incidence on the axis of the pipe T, in        cross-section, and they serve to detect the longitudinal        imperfections (for example P11, FIG. 3B). In this case the angle        of incidence in the cross-section is preferably selected in        order to generate in the pipe only transversal or shear        ultrasound waves, bearing in mind the characteristics of the        water/metal interface of the pipe (in principle water/steel).        Generally two sensors are provided, P11 and P12, with opposing        incidences in relation to the axis of the pipe (FIG. 2).

The machine also comprises sensors such as P21 and P22, the ultrasoundbeam of which, also in focus, on the other hand extends to the vicinityof a plane passing through the axis of the pipe, but has an incidence inrelation to the plane perpendicular to the axis of the pipe T (seesensor P21, FIG. 3C). In this case, the angle of incidence in relationto the plane perpendicular to the axis of the pipe is preferably chosenin order to generate in the pipe only transversal or shear ultrasoundwaves, bearing in mind the characteristics of the water/metal interfaceof the pipe (in principle water/steel). These sensors serve to detectthe transversal imperfections. Generally two sensors are provided, P21and P22, with opposing incidences in relation to the perpendicular planeof the axis of the pipe (FIG. 2).

Testing for imperfections generally takes place by focusing the beam.The focal point is measured in relation to the bond, which correspondsto the first outgoing and return trajectory of the ultrasounds in thethickness of the pipe. So, the sensor in FIG. 3A is focused athalf-bond, while the sensors of FIGS. 3B and 3C are focused atthree-quarters bond. Moreover, the testing for external imperfectionsgenerally takes place at the bond, and that for internal imperfectionsat the half-bond.

Ta is noted, this being the time required for the probe to be able tocorrectly receive the return ultrasound beam representing a possibleimperfection. This time Ta depends on the sum of the following twotimes:

-   -   firstly the outgoing and return propagation time of longitudinal        ultrasound waves, over the height of the water column present        between the probe and the pipe, along the trajectory of the        ultrasounds;    -   and secondly the propagation time of transversal ultrasound        waves, as required within the pipe to perform the        non-destructive testing itself. This time depends mainly on the        selected number of reflections of the transversal waves within        the wall of the pipe.

Conventionally, the probes are made to rotate around the axis of thepipe by means that are not shown, at a speed T of the order of severalthousand revolutions per minute (6,000 rpm, for example). In the case,also known to a person skilled in the art, where it is the pipe that isrotated while the probes are not made to rotate (so-called rotating pipeinstallation), the speed of rotation of the pipe is of the order ofbetween several tens and several thousands of revolutions per minute.

A cell is the name given to each sensor—transmission medium (water)—pipeassembly. For a cell, consideration must also be given to the beamopening Od of the detecting ultrasound probes. An opening can be definedwith two components (FIG. 1), one Od1 in the cross-section of the pipe,and the other Od2 in the plane passing through the axis of the pipe andthe probe.

Adjustment of the installation (as a function of the speed of rotation,the throughput speed, the dimensions Od1 and Od2 and the number ofprobes) should guarantee scanning by the ultrasound beams of all thesurfaces and volume of the pipe to be tested.

It should be noted that certain standards or customer requirements orspecifications state what the coverage of the scanned zones must be.

The analysis time Ta is therefore defined by a compromise between:

-   -   the rate (or frequency) of recurrence Fr, also known as pulse        repetition frequency (PRF);    -   in the cross-section of the pipe, the speed of rotation w,        taking into account the detection opening Od1 of the ultrasound        probes (in other words, bearing in mind the rotation of the        sensors, the component Od1 of the beam opening must allow a time        for the presence of the imperfection in front of the sensors        that is at least equal to Ta);    -   along the pipe, the speed of throughput v of this, bearing in        mind the detection opening Od2 of an ultrasound probe, and the        number NFi of probes dedicated to the same function Fi (which        therefore constitute a group of probes), around the periphery of        the pipe (in other words, bearing in mind the feed of the pipe,        the component Od2 of the beam opening must allow a time for the        presence of the imperfection in front of the sensor (or the        group of sensors) that is at least equal to Ta);    -   the number of probes dedicated to the same role (that is to say        the same function), and    -   the wave propagation times as defined previously.

Conventionally, the machine typically comprises a total of two sensorssuch as P11, P12 for testing for LD type and possibly ID typeimperfections, two sensors such as P21, P22 for testing for type CDimperfections, plus in principle one sensor of type P1, to measure thethickness of the product and test for type MD imperfections. Each sensormay in fact be a group of sensors working together, as will be seen.

The machine has either integrated or separate excitation and detectionelectronics associated with each of the sensors. It comprises (FIG. 4) apulse transmitter 70, for example at 250 Volts, for excitation of theprobe P0 mounted on the water box 100. As an integral part of thenon-destructive testing system, the ultrasound probe P0, here atransceiver, receives the echoes following this excitation. Lines 700and 710 transmit, respectively, the excitation pulse and the signal atthe terminals of the probe to an amplifier 73.

The output from the amplifier 73 serves as a display for the operatorand/or control of a sorting robot able to separate (downstream)non-conform pipes.

The display is, for example, performed on an oscilloscope 750, whichreceives as a signal the output from the amplifier 73, and as a timebase 752 a signal from a synchronisation stage 753 coming from thetransmitter 70. A threshold stage 754 avoids blinding of theoscilloscope at the time of the transmission pulse.

Another output from the amplifier 73 goes to a signal processing stage760. This processing generally comprises rectification, smoothing andfiltering. It is followed by a detection or selector phase 762, capableof isolating significant echoes in a known way. For detection of theimperfection, it is the presence of an echo, and its amplitude or itsduration (thus its energy), which are significant, in certain timewindows, essentially the half-bond and the bond. For detection ofthickness, a check is made that the distance equivalent of the timedeviation between the respective bottom echoes correctly corresponds tothe desired thickness of the pipe. Anomalies detected according to thesecriteria can be used to issue an alarm in 764, and/or to control asorting robot 766 which removes the non-conform pipes, marking these asa function of the anomaly or anomalies detected.

Physically in the case of a rotating head installation (FIGS. 5A and5B), the cell also comprises, on a mechanical support 80, the water box100, which houses a sensor assembly P0, with a connection 701, thatjoins the lines 700 and 710 of FIG. 4. Three rolling bearings 81 to 83are, for example, provided in order to centre the pipe T.

According to the known method (machine sold, for example, by the Germancompany GE NUTRONIK, formerly NUKEM), the sensor assembly P0 comprisessensors that rotate thousands of times per minute around the pipe. Anumber of sensors can also be used distributed in a ring around thepipe. The ring comprises, for example, 6 sectors of 128 ultrasoundsensors, distributed around the periphery. The sensor sectors have analternating slight offset in the direction of the axis of the pipe. Thisallows coverage between two consecutive sensor sectors longitudinallyand also reduces the problems of interference.

Interference occurs when a given sensor receives echoes due to a firing(ultrasonic shot) made on another sensor.

In addition to this there is a bench (not shown) for guiding the pipeupstream and downstream of the non-destructive testing station, in orderto accurately position the pipe which passes continuously past theultrasound sensors.

The non-destructive testing must be performed around the entireperiphery of the pipe. But it is also essential that this test monitorsthe linear speed v of the pipe as it leaves production. A compromise istherefore arrived at between the linear speed v of the pipe, the rate(or frequency) of recurrence Fr, also known as pulse repetitionfrequency (PRF), the analysis time Ta, the working opening Od of theultrasound probe during detection, and the speed of rotation ω, thenumber of sensors performing the same function and the speed ofpropagation of the ultrasound waves.

It is also desirable if the same installation is able to work across afull range of pipe diameters (and also pipe thicknesses), covering theproduction range. It is then common to provide several values of thespeed of rotation ω, and frequency of recurrence Fr, also known as pulserepetition frequency (PRF), which values are selected as a function ofthe diameter of the pipe to be processed.

Finally, it will be noted that any change to production will involve areadjustment of the angles of incidence of the ultrasounds of eachsensor on the periphery of the pipe. This delicate operation, which isperformed manually, currently takes around half an hour, during whichtime production of pipes is halted. Such are the conditions under whichnon-destructive testing by ultrasounds of pipes or other profiled and/orthin-walled products as they leave production currently takes place.

In the area of ultrasound non-destructive testing, the followingterminology is often employed:

-   -   “scan” means a sequence of relative pipe/sensor positions;    -   “increment” means the scanning pitch (inversely proportional to        the frequency of recurrence, also known as pulse repetition        frequency (PRF), or the ultrasound firing (shot) frequency);    -   “Ascan” means the graph of the electrical voltage measured at        the terminals of an ultrasound sensor, with time of flight on        the abscissa and a representation of the electrical voltage,        also referred to as ultrasound amplitude, on the ordinate;    -   “Bscan” means an image relative to a given value of the        increment, with the scan corresponding to the ultrasound firing        (shot), possibly expressed in degrees as the angle of the sensor        in relation to the part to be inspected, on the abscissa, and        the time of flight on the ordinate, and at each point the        ultrasound amplitude converted to grey or colour scale;    -   “Echodynamic” means a curve (graph) with an indication on the        abscissa of the ultrasound firing (shot) and on the ordinate the        maximum amplitude detected in a time selector of the Ascan for        the corresponding firing (shot);    -   “Cscan” means an image with, on the abscissa and the ordinate,        the equivalent position in a flat space of the point (scan        position) of firing (shot) of the ultrasound wave and        representing, converted into grey scale, the maximum ultrasound        amplitude for this firing (shot) detected in the time selector        considered of the Ascan (image amplitude). In the case of a        pipe, a point on the abscissa of the Cscan corresponds to a        position on the length of the pipe and a point on the ordinate        to a position on the circumference of the pipe. In the case of a        flat product, a point on the abscissa of the Cscan corresponds        to a position on the length of the flat product and a point on        the ordinate to a position on the width of the flat product.

Furthermore, the applicant uses in the remainder of the specificationthe following terms:

-   -   “parallelepipedic 3D Bscan” which designates a 3D representation        comprising in addition the position of the sensor on the axis of        the pipe, the representation being considered as rough and the        form of the tube not appearing;    -   “reduced 3D Bscan” which designates a parallelepipedic 3D Bscan        limited to a zone with an ultrasound indication of a probable        defect at the end of the filtrations;    -   “pipe 3D Bscan” which has the same dimensions as the        parallelepipedic 3D Bscan, the data being represented in the        pipe inspected, the amplitude possibly being able to constitute        a supplementary dimension.

FIG. 6 is a schematic longitudinal cross-sectional view of a systemcomprising a sensor, its water column and the pipe, showing the variousultrasound trajectories forming echoes. It allows a good understandingof the complexity of these trajectories and the difficulty of theanalysis.

FIG. 6A is a schematic amplitude/time diagram of the ultrasound signalat the level of a sensor working under oblique incidence. From theinstant Texcit of excitation of the sensor, there is a water-pipeinterface echo at instant Tinterf (which can also be referred to asTphiExter0). Then there is marking (vertical dotted line) of the instantTphiInter when the ultrasound beam reaches the inner skin of the pipe,where it reflects and refracts, as well as the instant TphiExter1 whenthe ultrasound beam reaches the outer skin of the pipe. As a result ofthe oblique incidence, there is no significant reflected echo thatreturns to the sensor in TphiInter in the absence of an imperfection atthis spot. This also applies at TphiExter1.

FIG. 6B is a schematic amplitude/time diagram of the ultrasound signalat the level of a sensor working under normal incidence. The generalchronology of the signals is the same as for FIG. 6A (except for afactor associated with the incidence). On the other hand, under normalincidence, there are significant echoes in TphiInter and in TphiExter1,even in the absence of an imperfection at the points of the pipeconcerned.

The present day non destructive testing systems used in the productionof pipes operate by establishing a ratio K between:

-   -   the amplitude As of a signal coming from the pipe to be        inspected, and    -   the amplitude A0 of the signal coming from a standard reference        defect, for the type of test concerned. This “standard reference        defect” is in general defined on a reference pipe carrying an        artificial defect (for example a U- or V-shaped notch) with        selected dimensional characteristics, for example in accordance        with a non-destructive testing standard and/or customer        requirements.

The implied assumption is that this signal amplitude is proportional tothe criticality of the imperfection, i.e. to its depth (DD). The graphof FIG. 7 (well known to a person skilled in the art, see NondestructiveTesting Handbook—statistics section of volume 7 published by theASNT—American Society for Nondestructive Testing) represents the realdistribution K=f(DD). It shows that in reality the correlation is verypoor (of the order of 0.3 to 0.4 for ultrasound testing).

More specifically, in the graph of FIG. 7, if the reference amplitude A0(K=1) is fixed at the value XL (maximum acceptable depth ofimperfection) at the centre of the distribution (itself centred on theoblique TDis), it can be seen that imperfections can still be found atK=0.5 with a depth DD of greater than XL. It follows that, to be on thesafe side, it is necessary to set A0 at a much lower value than XL. As aconsequence, in production, pipes will be discarded which, however,would in fact be satisfactory. This is all the more disastrous,economically, as pipe manufacture involves heavy engineering which isboth complex and energy-intensive.

The applicant has therefore devoted much effort to improving thesituation.

FIG. 8 shows an improved device compared to that of FIG. 4.

The output of the amplifier 73 is applied to a stage 761, whichdigitises the amplitude of the signal coming from the amplifier 73, andworks on this digitised signal. This processing will be described in thefollowing by reference to FIG. 11. Stages 764 and 766 which arefunctionally similar to those of FIG. 4 can then be retained. The rawsignal of the sensor, as can be seen on the oscilloscope 750, isreferred to as Ascan by persons skilled in the art. It includes echoesaccording to the diagram defined by FIG. 6.

It is desirable to perform imaging of the pipe imperfections with thehelp of ultrasound signals. A description is now provided of how animage is obtained.

In practice an image is obtained by considering several successive scansof the pipe by a sensor Px, under successive angles which roughly covera cross-section of the pipe. It is possible to do this by successivefirings (shots) from a single sensor, using the relative rotation of thepipe/sensor.

By way of example, and without being restrictive, it is a case here ofan installation of the so-called rotating head type.

In FIG. 8A, a sensor Px is considered, which can be one of the types P1,P11, P12, P21 and P22 mentioned above. In the example shown, this sensorPx comprises in fact n elementary sensors Px-1, Px-i, Px-n, which arealigned along the longitudinal axis of the pipe, and which are theobject of an ultrasound firing (shot) at the same time. In FIG. 8A, thatwhich is between the elementary sensors and the 3D graph of output 769can be considered to be a converter.

The Ascan signal from the first elementary sensor Px-1 is applied to anamplifier 73-1, followed by two parallel channels: that of selector763-1A and that of selector 763-1B. Each selector 763-1A comprises twooutputs of the maximum amplitude and time of flight respectively. Themaximum amplitude output is connected to a line digitiser 765-1A. Thetime of flight output is connected to a line digitiser 765-1At.

The output of the line digitiser 765-1Aa of the maximum amplitude isconnected to a data buffer store 768-Aa that collects the data comingfrom the maximum amplitude line digitisers with an index i that runsfrom 1 to n. The output of line digitiser 765-1At of the time of flightis connected to a data buffer store 768-At that collects the data comingfrom the time of flight line digitisers 765-iAt with an index i thatruns from 1 to n. The output of the line digitiser 765-1Ba of themaximum amplitude is connected to a data buffer store 768-Ba thatcollects the data coming from the maximum amplitude line digitisers765-iBa with an index i that runs from 1 to n. The output of linedigitiser 765-1Bt of the time of flight is connected to a data bufferstore 768-Bt that collects the data coming from the time of flight linedigitisers 765-iBt with an index i that runs from 1 to n.

On the basis of the information obtained as the reference pipe is passedthrough, the operator can enter in the buffer stores 768-Aa ad 768-Atthe information T_1A corresponding to an indication of the position andthe time width, which provides it, as a function of the known geometryof the pipe, with the instants where he will find an “inner skin echo”,relating to the inside of the pipe, for example the first echo Intl ofFIG. 6. FIG. 6A shows more clearly the corresponding time window “Int”,around TphiInter.

Similarly, on the basis of information obtained as the reference pipepasses through, the operator can enter in the buffer stores 768-Ba and768-Bt the information T_1B corresponding to an indication of theposition and the time width, which provides it, as a function of theknown geometry of the pipe, with the instants where he will find an“outer skin echo” relating to the outside of the pipe, for example thefirst echo Ext1 of FIG. 6. FIG. 6A shows more clearly the correspondingtime window “Ext”, around TphiExter.

The diagram is repeated for the other sensors Px-2, . . . , Px-i, . . .Px-n.

So, each time selector 761 defines time windows taking into account theinstant of transmission of the ultrasounds, and pre-definable timeintervals where there can be expected to be echoes concerning thisselector. The illustration of FIG. 6 shows how it is possible to definethe time intervals of interest, taking into account the angle ofincidence of the ultrasound beam on the pipe, as well as the diameter(internal or external) and the thickness of the pipe. A given timeinterval corresponds to a given echo at a given point of the pipe, for agiven relative position between the pipe and the sensor.

For simplification, it is assumed here that the firing (shot) instantsare synchronised with the relative rotation of the pipe/sensors, so thatan elementary sensor always works on the same longitudinal generatingline of the pipe. The output of its selector thus provides a spaced outsuccession of analogue signal samples, which each correspond to theamplitude of an echo expected on a wall of the pipe. These samples ofsensor Px-1 (for example) are digitised in 765.

Synchronisation with the transmission can be ensured by a link (notshown) with the transmitter 70, or with its trigger, the synchronisationcircuit 753, or its time base 752 (FIG. 8). The display 750 can bemaintained, if desired. The system can function on a pipe rotating atroughly constant speed. In this case, the angular speed and the feed ofthe pipe can be measured with the help of an accurate angle encoder, forexample model RS0550168 supplied by the Hengstler company, and a laservelocimeter, for example model LSV 065 supplied by the company Polytec.The pipe may also not be rotational, whereas the system of sensorsturns. In this case, the laser velocimeter is sufficient for measuringthe feed of the pipe, while the speed of rotation of the sensors isknown by means of an angle encoder.

For a given firing (shot), the set of sensors Px-1 to Px-n provides animage line that corresponds to a cross-section of the pipe. In the otherdimension of the image, a given elementary sensor provides a line whichcorresponds to a generating line of the pipe.

The digitisers 765-1Aa, 765-2Aa, . . . , 765-iAa, . . . , 765-nAa and765-1At, 765-2At, . . . , 765-iAt, . . . , 765-nAt allow an “internal”image, relating to the inner skin of the pipe to be filled. Thedigitisers 765-1Ba, 765-2Ba, . . . , 765-iBa, . . . , 765-nBa and765-1Bt, 765-2Bt, . . . , 765-iBt, . . . , 765-nBt allow an “external”image, relating to the outer skin of the pipe to be filled, with Tvolmax being the time of flight of the maximum amplitude echo.

The parallelepipedic 3D graph stored in 769 constitutes the sensor orgroup of sensors Px concerned. Each point of this image corresponds,transposed into shades of grey, to a value of the amplitude of the echodue to the reflection of the ultrasound signal on a possibleimperfection in the zone of the pipe concerned. This value can alsorepresent the ratio between the maximum amplitude of the ultrasoundsignal captured on the pipe during the test and the maximum amplitude ofthe ultrasound signal obtained with an artificial “standard referencedefect”, as defined above. The parallelepipedic 3D graph is arepresentation of the preparatory 3D Bscan digitised in 769—preparatoryin the sense that it serves as the basis for generation of the pipe 3DBscan. The form of the 3D graph is generally different from the form ofthe product examined, in particular for pipes.

The data of the parallelepipedic 3D graph can comprise the set ofpairings (time of flight, amplitude) of the Ascan curve (graph) over agiven digitisation period.

The parallelepipedic 3D graphs digitised in 769 comprise theparallelepipedic 3D graphs 891 constructed from the data originatingfrom a group of sensors P11 and the parallelepipedic graphs 892constructed from the data originating from a group of sensors P12 andP21 and P22 respectively as shown in FIG. 11.

This image now corresponds to a zone of the pipe, obtained by joiningtogether roughly annular zones of the pipe corresponding to each of thedigitised lines. In fact, it is a case of annular or helical zones ifthe ultrasound beam is applied roughly perpendicularly to the axis ofthe pipe. It is known that the case differs according to the relativemovement of the pipe/sensor. The zones are then rather more ellipticaland, as a result, warped or twisted in space. In the presentdescription, the expression “annular zones” covers these variouspossibilities.

It should be noted that in order to obtain this complete restoration ofthe 3D graph, the additional information on the positioning of thesensor in relation to the pipe is required. It is available on aseparate input 740. This information comes from an encoder or a set oflasers allowing measurement of the spatial position. As the pipe can belikened to a cylinder without any thickness, the positional informationcan be reduced to two dimensions.

It is understood that the implementation of the invention on an existingultrasound test bench involves:

-   -   accessibility to the ultrasound testing raw data, which is        provided, for example, with the help of a data acquisition card,        such as model NI 6024, series E or NI 6251, series M, from the        company National Instrument, or by direct access to the digital        data contained in the bench test electronics;    -   availability of on-line information on the speed of rotation (of        the pipe or of the sensor head) or the relative angular position        of the pipe in relation to the sensor, and    -   availability of on-line information on the pipe feed speed or        the relative linear position of the sensor projected onto the        axis.

The diagram of FIG. 8A can be applied:

-   -   in parallel to a sensor of type P11 and a sensor of type P12,        observing the same zone of the pipe from two different        directions. Each sensor will allow an internal image and an        external image to be obtained. Then, one of the images may be        selected as a function of a command with the notation “Int/Ext”;    -   in parallel to a sensor of type P21 and a sensor of type P22,        which, here again, will each allow an internal image and an        external image to be obtained.

The diagram in FIG. 8A can also be applied to a sensor of type P1, inwhich case three parallel channels are provided behind each amplifier(at least virtually). One of these channels operates in a repetitivetime window positioned as indicated under “Volum.” in FIG. 6B. Thischannel allows a check of imperfections in volume, that is to say in thethickness of the pipe.

The two other channels can operate respectively in repetitive timewindows positioned as shown in “WphiExter0” and in “WphiInter1” in FIG.6B. These two other channels allow measurement of the thickness of thepipe.

The distinction between the 3 channels is purely functional (virtual).In fact, the aforementioned two other channels can be physically thesame, in which there is discrimination of the instants or windows“WphiExter0” and “WphInter1”. It is also possible to use a singlephysical channel, in which there is discrimination of the instants orwindows “WphiExter0”, “Volum.” and “WphiInter1”.

It is representative to describe in more detail the case of a sensor oftype P11 with a sensor of type P12. This is what will be done now.

It will be recalled that these two groups of sensors P11 and P12 areused for detection of longitudinal imperfections in pipes. Ultrasoundtesting is performed with ultrasound firings (US shots) in two preferreddirections (clockwise—counter-clockwise):

-   -   a sensor or group of sensors P11 provides an ultrasound image of        the pipe in a working direction (clockwise);    -   a second sensor or group of sensors P12 provides an ultrasound        image of the same pipe in another working direction        (counter-clockwise).

So the longitudinal imperfections are advantageously detected with 2sensors or groups of sensors whose beam axes are inclined symmetricallyin relation to a plane perpendicular to the axis of the pipe. Theinclination is, for example, approximately ±17°. This provides anexample of the application of the system with two sensors, or two groupsof sensors, as mentioned above.

In the embodiment of FIG. 8B, each digitisation window 782 deriving froman amplifier 781 can be characterised by a start, a duration and adigitisation frequency that define a number n of points of the Ascansignal considered. Each digitisation window 782 then provides a number nof pairings of information (amplitude, time of flight) for eachultrasound firing (shot). The buffer/multiplexer 788 places all the datacollected in this way into the parallelepipedic 3D graph 769 taking intoaccount the respective positions of the sensors at the moment when thesignal is received, all at once thanks to knowledge of the geometricalconfiguration of the sensors in relation to one another and thanks tothe information on the pipe/sensor positioning at the time of theultrasound firing (shot) 740.

Reference is now made to FIG. 9. For the first test direction(“direction 1” tab selected), images 903 and 904 are cross-sectionalviews (transversal and longitudinal, respectively) of the pipe 3D Bscan,3D with the geometry of the pipe, as described further on, coming fromthe sensors P11. The positioning of these cross-sections is fixed usingthe “transversal cross-section at (mm)” and “longitudinal cross-section(degrees)”. The images 905 (internal) and 906 (external) are Cscans, asdefined above, image 905 (or 906) being concentrated on a time zone ofthe Ascan where the imperfections in the inner (or outer) skin aresupposed to have been detected. The information necessary forreconstruction of the images 905 and 906 come from the parallelepipedic3D Bscan 891 of FIG. 11.

The image 901 is a 3D representation projected onto the pipe 3D Bscan ofa portion of the product to be tested, in which position the zones ofpotential interest are identified, as described further on. The sameimages 903 bis, 904 bis, 905 bis, 906 bis and 902 are recreated for thesecond test direction (“direction 2” tab active), see FIG. 9A.

We would reiterate at this point that the above description concerns thedetection of defects with a longitudinal orientation. The same approachapplies to the investigation of transversal defects (with groups ofsensors P21 and P22).

Reference is now made to FIG. 11. The image blocks 901 and 902 areobtained from the parallelepipedic 3D graphs 891 and 892 by means oftransformer unit 930 as detailed in FIG. 11A. The converter unit 891 ofFIG. 11 corresponds to the set-up of FIG. 8A, applied to the sensor P11.Similarly, the converter unit 892 also corresponds to the set-up of FIG.8A, but applied to sensor P12. The converter blocks 891 and 892 use thepipe/sensors contextual data of block 740. These data relate to thecharacteristics of the pipe under examination and the sensors currentlyin use.

Transformer unit 930 is arranged downstream of the parallelepipedic 3Dgraphs 891 and 892 and can have the structure shown in FIG. 11A. Thetransformer unit 930 performs a time calculation of the passage of thewave propagation in the pipe taking into account the mode conversion atthe time of impact of an ultrasound on a defect. Upon impact atransversal wave can be transformed into a longitudinal wave and viceversa. The transformer unit 930 can estimate the propagation of power ofthe acoustic beam from calculations of transmission and reflectioncoefficients. An analysis of the frequency spectrum of the Ascan can becarried out. The transformer unit 930 can comprise a database 939 ofreal or simulated tests to allow a comparison with the 3D graphsreceived. The transformer unit 930 can recreate the 3D Bscan image withthe geometry of the pipe.

As illustrated in FIG. 11, the transformer unit 930 comprises two units931 and 932 for removing unnecessary zones of 3D Bscans from a 3D graph,unit 931 processing data from the 3D images 891 and unit 932 processing3D images 892, two units 933 and 934 for filtering by application of asimulated time window, downstream, respectively, of units 931, 932, atheoretical simulation unit 935, and a tolerance calculation unit 937supplying an inverse algorithm unit 936, unit 936 providing the images901 and 902 defined above.

The removal by units 931 and 932 allows a reduction in the quantity ofinformation processed, while retaining zones of potential interest to beshown in three dimensions. The filtering can be performed by length onthe basis of a Cscan. The length selected may be greater than the lengthof a zone with an amplitude greater than a threshold. Theparallelepipedic 3D Bscans including a zone with a potentialimperfection can then be processed.

Filtering by units 933 and 934 can be performed by demarcating the timewindow by the interface and bottom echoes. These filter units can alsodemarcate the angular zone of the pipe of potential interest and ifnecessary offset these zones in order to define and fully recreate thezone of potential interest. The images provided by units 933 and 934 arereduced 3D Bscans.

The theoretical simulation unit 935 can comprise a simulations database,for example of 3D Ascans or Bscans as a function of the types andposition of the defects. The database can comprise simulated resultsand/or results from tests on natural and/or artificial defects.

The inverse algorithm unit 936 can compare theoretical 3D Ascans orBscans provided by the theoretical simulation unit 935 and 3D Ascans orBscans obtained during the inspection in order to determine the closesttheoretical Ascan or Bscan and, as a consequence, the most likelydefect(s). By way of example, the inverse algorithm unit 936 compares afiltered experimental Ascan corresponding to a length position and to anangular position with theoretical Ascans on this same position in lengthand evolute. By way of example, the inverse algorithm unit 936 comparesa 3D Bscan resulting from a reduced 3D Bscan corresponding to a lengthposition with the theoretical 3D Bscans on this same length position.The two comparisons can be made. The best set of theoreticalrepresentations of the echoes is then the set that has the smallestdeviations from the experimental data.

After transformer unit 930, filters 921 and 922 are shown, see FIG. 11,which in particular allow extracts to be taken from images, and fromtheir preparatory data, as input data combined by the combiner unit 960for neural or expert processing 970.

In the embodiment described, filter 921 has:

-   -   a signal output Zcur designating a working zone in the image.        This output is used by an extraction function 951 which as a        consequence performs an extraction from the image (Cscan) for        the Zcur zone, and an access to the image preparation 891 in        order to obtain information stored there (so-called Ascan),        relating to the same Zcur zone. All these data are transmitted        by the extraction function 951 to the combiner 960, as inputs to        the neural or expert processing 970;    -   an output providing information obtained by filtering, some at        least relating to the zone Zcur, which it transmits as input for        the neural or expert processing;    -   optionally (dashed line) outputs of additional filtered data to        a memory 990.

The same applies to filter 922, with the extraction function 952, forthe same Zcur current zone.

The neural system 970 supplies a decision and alarm circuit 992, whichcontrols a sorting and marking robot 994. An operator interpretationinterface 996 can be provided, which can present all or part of the datacontained in the memory 990, in relation to the section of pipe underexamination. The data contained in the memory 990 come from filters 921and 922.

Apart from its prediction (origin, type and severity of the indication)the neural system 970 provides an assessment of the confidence that canbe attached to this prediction. This information is accessible tooperators who also have available more qualitative data such as thebackground to the order in progress or problems that have occurredduring construction of the product. The operator or a specialist canthen be involved to weight the predictions accordingly.

Here, FIG. 11 deals with information coming from at least two groups ofsensors providing the same function or intended for the same type oftesting (the 2 groups P11 and P12 or the 2 groups P21 and P22). The samediagram can be used to handle the information coming from a largernumber of sensor groups intended for different types of tests. Thenumber of images processed simultaneously is increased by the sameamount.

The primary function of the filters 921 and 922 is to determine theimperfection zones in the Cscan images 901 and 902. Generally speaking,the filtering is arranged in order to pinpoint the zones to be analysedand to distinguish there the imperfections from other indications. Thefiltering works on two equivalent portions of the two images. The twofilters can work in conjunction.

By scanning the digital image, to begin with, the areas of the image areidentified where there are potential imperfections. A fixed thresholdestablished by calibration can be applied for this purpose.

A threshold can be used that adapts to the prevailing noise level in theimage. The method is based on the theory of the detection of a signal ina white noise which can be based on two hypotheses:

-   -   Hypothesis H0: measurement=white noise of mean m_b and standard        deviation std_b    -   hypothesis H1: measurement=signal+white noise

Statistical tests are performed which allow a determination of whetherthe situations fall within the realm of hypothesis H0 or hypothesis H1.These statistical calculations are performed in real time on n slidingpoints of the image corresponding to consecutive firings (shots). Thenumber n can be determined by learning.

According to this method (so-called Gaussian addition), it is, forexample, possible to use the Neyman-Pearson criterion to determine adetection threshold according to a given probability of false alarm(pfa). This is expressed by the attached formula [21]. The Gaussiancumulative function, generally known as Q (or also the error functionerf) is used, which it is necessary to invert in order to obtain thethreshold, according to the appended formula [22].

In practice the presence is frequently noted of background noise thatmay have various origins (for example: presence of water inside thepipe, electrical interference, acoustic phenomena due to the structureof the material of the product under test). The use of a variablethreshold avoids the false alarms that occur if a fixed threshold isapplied.

Among the other false indications that are likely to appear,interference occurs in the form of very short peaks in the ultrasoundsignal. This interference can be removed by simple algorithms that canbe referred to as cumulative counting algorithms or also integrators(example: “n times before alarm” or “double threshold”).

The applicant has also considered the ‘turn’, which is the trajectoryfollowed by the sensor along the cylindrical surface to which the pipeis likened. Filtering can be performed along each turn in order tofurther reduce the rate of false alarms. To this end use is made, forexample of a Butterworth filter and/or a discrete Fouriertransformation, such as a rapid Fourier transformation. This method isapplied to each digital line.

The same type of algorithm can be applied in the longitudinal directionof the pipe.

In this way potential imperfections are located. Once an imperfectionhas been pinpointed its position corresponds to the position analysed inthe images of FIG. 9 (for example), with a 3D image, a transversalcross-section and an axial cross-section. The radial position/thicknessindications (or, more simply, the position of the imperfectioninternally, externally or in the mass) can be represented as attributesof the points of the image. Thus, we have:

-   -   two 2D images representing the possible imperfections in the        outer skin of the pipe;    -   two 2D image representing the possible imperfections in the        inner skin of the pipe, and    -   one 2D image representing the possible imperfections in the        thickness of the pipe.

The imperfections are now deemed to be “confirmed” following eliminationof interference and false alarms, in particular.

Following on from this the applicant has now decided to work on an imagezone of fixed size. It is therefore necessary to align this zone withthe data on the imperfection existence data that have just beenobtained.

In other words, it is necessary to position the points that have beenidentified as being greater than the threshold in order to determine thecomplete zone around an imperfection. This is necessary, for example, ifit is desired to determine the obliquity of an imperfection.

The algorithm goes through a number of steps:

-   -   contour detection (Roberts gradient, for example);    -   dilation (gathering of near contours);    -   erosion, then closure, which allow determination of a mask        around the imperfections;    -   a final surrounding stage allowing full localisation of the        imperfection.

Thus for each imperfection the coordinates are obtained of thecorresponding image zone, which will be useful for the neural networkanalysis that takes place next.

FIG. 12 illustrates this processing of the image zones in the form of aschematic view.

At the start of the images (801), there are between zero and p imagezones to be processed representing a confirmed imperfection. Operation803 assumes that there is at least an initial zone, which serves as thecurrent zone for processing Zcur in 805. For this zone Zcur:

-   -   operation 807 selectively extracts data from images 901 and 902        which correspond to this zone (defined by its coordinates in the        image);    -   operation 809 selectively extracts data which have played a part        in the preparation of the images 901 and 902, and which        correspond to zone Zcur. Examples of these data will be provided        below;    -   operation 811 performs the neural processing properly so-called,        more of which later;    -   the results obtained for zone Zcur are stored selectively in        813, corresponding to a Zcur zone designation;    -   test 820 looks to see if there is another zone to be processed        in the image, in which case a restart is made in 805 with this        other zone as indicated in 821; if not, the processing of the        current image(s) is terminated (822).

In the case of the processing of sensor P1, there is only one image,which changes the number of input parameters. Apart from this, theprocessing can generally be the same.

Following determination of each zone of interest Zcur, the filtering cancomprise other functions. For these other functions, FIG. 13 illustratesin a schematic way the interaction between the filtering and the seriesof operations shown in FIG. 11.

FIG. 13 is similar to FIG. 11, but only for image 901. It shows:

-   -   the pipe-sensors contextual elements of block 740;    -   the extractor 951 which finds the data for the Zcur zone, in        image 901 and its preparation 891;    -   an inner/outer block 7410, indicating if the imperfection in the        Zcur zone considered is located in the inner skin or outer skin.

That added to the base data by the filtering is defined in more detail,that is, for each Zcur zone (block 805), as shown by the contents of thebox with a dashed line:

-   -   investigation of the angle of obliquity in 941;    -   indication of the length of the imperfection 942.

In addition to the following, in particular, may be included:

-   -   an alignment indication in Cscan, in 945, and    -   in 946, an indication of the existence of other imperfections in        the same cross-section of the pipe.

In the embodiment described, the data such as 945 and 946 go to memory990. The other data go to the neural networks or expert systems 970.These are separated here into two functions, as will now be seen.

An imperfection in the pipe can be defined by its position, its type andits severity, often likened to its depth. In the embodiment described,the type and degree of depth of a pipe imperfection are determinedseparately with the help of two neural processes of the same generalstructure, which will be detailed now using an example.

The case of the imperfection type is dealt with according to FIG. 14,and that of the severity according to FIG. 15.

The types can be defined, for example as illustrated in FIGS. 10A to10D. These figures illustrate four types, which represent a simplifiedchoice compared to the list of imperfections supplied by the API andwhich can be caused by pipe construction processes. The headings inFrench and English are those used by persons skilled in the art todesignate the type of imperfection. It will be noted that imperfectionstypes 1 and 3 are straight and those of FIGS. 2 and 4 arc-shaped(“chord”).

A correspondence between the actual imperfections and the four abovetypes can be defined as follows:

Name in French Name in English Assignment Entaille Notch TYPE 1 TapureCrack TYPE 1 Paille/repliure perpendiculaire ou Seam TYPE 1 droite(laminage) (perpendicular) Paille/repliure (laminage) Seam (arcuate),TYPE 2 “overlap” Gravelure Sliver TYPE 3 Origine billette Rolled-in-slugTYPE 4 Rayure Gouge TYPE 4 Inclusion Inclusion TYPE 4 Manque de matière(“défourni”) Bore-slug TYPE 4 Chevauchement/recouvrement/repliure LapTYPE 4

Here, FIGS. 14 and 15 both use neural circuits with three intermediateneurons (or “hidden neurons”), referred to as NC121 to NC123 for FIG. 14and NC141 to NC143 for FIG. 15.

FIGS. 14 and 15 have a certain number of inputs in common. As an aid tounderstanding, the inputs are illustrated using different types oflines. Double lines indicate that the inputs are multiple, that is tosay repeated for each point of the Zcur zone.

To begin with, in 7410, according to the status considered by theselectors 763 concerned, information is provided indicating if it is acase of processing an imperfection located in the inner skin or outerskin of the wall of the pipe. This information can also be obtained fromthe 3D Bscan.

The second category of common input variables includes contextualvariables, coming from block 740 (FIG. 13):

-   -   in 7401, WT/OD, which is the ratio of the wall thickness to the        pipe diameter;    -   in 7402, Freq, which is the frequency of operation of the        ultrasound probes;    -   in 7403, ProbDiam, which is the useful diameter of the        ultrasound probes.

The third category of common variables corresponds to the quantitiesresulting from the filtering, which can be considered common to the twosensors 921 and 922 (or more). An average is taken, for example, of theresults from the two sensors, or the most representative result(maximum/minimum, as the case may be) is taken. These quantities are thevariables in 9201, the obliquity of the defect, and in 9202, its length.These two variables are easy to pinpoint in the two images of FIG. 9,which have a mirrored symmetry.

Reference is now made to FIG. 14 only. The following category ofvariables includes variables of different measurements for each of thetwo sensors (or groups of sensors), and for each of the Zcur zones,which is reflected in the drawing by the use of a double line.

For a first sensor, we have:

-   -   in 9511, K1, which is the ratio between the maximum amplitude of        the ultrasound signal encountered in the Zcur zone and in image        901, to the maximum amplitude of the abovementioned “standard        reference defect”. In fact, in the example, the amplitude in        each pixel of the image 901 is defined by this ratio; K1 is then        simply the amplitude maximum encountered in the Zcur zone of        image 901; note Pmax1, the point of the Zcur zone where this        maximum is encountered;    -   in 9512, QBE1 which is a variable of the Cscan referred to as        QuantBumpsEchodyn, representing the number of local maxima        encountered in the Zcur zone of image 901 in the vicinity of        point Pmax1 of maximum amplitude. This number QBE1 is limited to        the local maxima encountered in the vicinity of Pmax1, either        side, but without the signal amplitude falling below a level        corresponding to the background noise. QBE1 will generally take        either the value 1 or the value 2.

These two variables come from image 901, via the extractor 951, which isshown by the notation 951(901) in the drawing. Added to this we have:

-   -   in 9518, RT1 which is a variable representing the echo rise time        in the native ultrasound signal known as Ascan, (this is the        difference between the moment when the signal is at its maximum        and the last previous moment when the signal is at the level of        the background noise commonly expressed in microseconds). This        variable RT1 has previously been measured at the output of the        amplifier 73 concerned (FIG. 8A); it has been stored, for        example in 891, in correspondence to the point of the pipe to        which it relates. It is in this way that it can be selectively        retrieved by the extractor 951. The variable RT1 can now be        directly measured by the operator on the image 903 of FIG. 9, or        also on the parallelepipedic 3D Bscan.

For the second sensor, we have:

-   -   in 9521, K2, which is defined like K1, but for image 902 instead        of image 901. In the example, K2 is simply the amplitude maximum        encountered in the Zcur zone of image 902; note Pmax2, the point        of the Zcur zone where this maximum is encountered;    -   in 9522, QBE2 is defined like QBE1, but in image 902 instead of        image 901, and in the vicinity of Pmax2. There again, QBE2 will        generally take the value 1, or the value 2.

These two variables come from image 902, via the extractor 952. Added tothis we have:

-   -   in 9528, RT2 which is a variable representing the echo rise time        in the native signal known as Ascan. As before, this variable        RT2 has previously been measured at the output of the amplifier        73 concerned (FIG. 8A); it has been stored, for example in 892,        in correspondence to the point of the pipe to which it relates.        It is in this way that it can be selectively retrieved by the        extractor 952. The variable RT2 can now be directly measured by        the operator on image 903A of FIG. 9, or also on the        parallelepipedic 3D Bscan.

The final input 958 of the neural network is a constant value, referredto as ConstantA, which represents a constant determined at the time ofcalibration of the model and resulting from learning.

The output 998 of FIG. 14 is a variable that is indicative of the typeof imperfection and its average inclination (defined as a function ofthe type).

The case of the degree of depth (or severity) of the imperfection isdealt with according to

FIG. 15. The inputs are the same as for FIG. 14, except:

-   -   for the first sensor, block 9512 is replaced by a block 9513,        which processes a variable EW_1, or EchodynWidth, which is the        width at mid-height (50%) of the echodynamic waveform, for this        first sensor. This variable EW_1 is drawn from the Cscan;    -   similarly, for the second sensor, the block 9522 is replaced by        a block 9523, which processes the variable EW_2, or        EchodynWidth, which is the width at mid-height (50%) of the        echodynamic waveform for this second sensor;    -   in 959, the constant, now referred to as ConstantB, is        different;    -   the output 999 is an indication of the severity of the        imperfection, referred to as DD.

It is of interest to note that, in both cases (FIGS. 14 and 15), a givenneural circuit 970 processes an image extract 951 for one of the groupsof ultrasound sensors, as well as an image extract 952 corresponding tothe same zone, but originating from another group of sensors.

The applicant observed that it was possible to obtain highlysatisfactory results, subject to a suitable adjustment of the parametersof an expert system, for example of the neural circuits, and possiblythe number of these, to optimise the prediction.

Moreover, the applicant found that by a combination of the informationgathered by the various neural networks, it was possible to furtherrefine the prediction.

Overall, the input parameters of the neural network or of the expertsystem are then characteristics of the two 3D images (ratio of the maxamplitude to the reference amplitude, echo width, orientation of theecho representing the obliquity of the imperfection, etc.) and of thetest (sensor, dimensions of the pipe, etc.).

The output parameters are the characteristics of the imperfection(depth, inclination/type). The decision and/or alarm (992) can takeplace automatically with the help of selected decision criteria, on thebasis of thresholds, carrying a degree of safety according to the need.In order to define these thresholds results from the learning can beused.

Reference is now made to FIG. 16, which is a model of the elementaryneural circuit of FIGS. 14 and 15, for two sensors.

This model comprises an input layer or level IL, which groups togetherall the input parameters (often called “input neurons”). In order not tooverload the diagram, only three neurons E1 to E3 are shown, plus aconstant, which can also be considered to be a neuron E0. This constantis most often referred to as the “bias”. In practice there are moreinput neurons, in accordance with FIG. 14 or FIG. 15, as the case maybe.

Then at least one hidden layer or level HL is provided, which comprisesk neurons (of which only 2 are shown in order not to overload thedrawing).

Finally comes the output neuron S1, which provides the decision, in theform of a value representing the importance of an imperfection in thepipe, for example a longitudinal imperfection. This output correspondsto block 998 in FIG. 14 and 999 in FIG. 15.

It is of interest to note that the “neuron” constant E0 comes into playto weight not only the hidden layer or layers HL, but also the outputneuron (output layer, OL).

The general behaviour of a neural circuit as used here is given byformula [11] of Annex 1, where w_(ij) is the weight assigned to thesignal Xi present at the input of neuron j.

In the circuit provided for here, an elementary neuron behaves accordingto formula [12], as shown diagrammatically in FIG. 17.

The output S1 of FIG. 16 provides an estimated value that corresponds toformula [13] of Annex 1.

By learning the applicant has adjusted the hidden neurons and theirweights such that the function f is a non-linear, continuous, derivableand restricted function. The example currently preferred is thearc-tangent function.

It is known that a neural network determines its coefficients w_(ij),commonly known as synapses, by learning. The learning must typicallyinvolve between 3 and 10 times more examples than there are weights tobe calculated, while correctly covering the desired range of workingconditions.

Starting with examples E_(p) (p=1 to M), for each example the deviationD_(p) is determined between the value S_(p) given by the neural circuitand the actual value Rp measured or defined experimentally. This is whatis reflected by formula [14].

The quality of operation of the neural circuit is defined by a globaldeviation variable Cg, known as “cost”. It can, for example, beexpressed according to formula [15] as a weighted quadratic globaldeviation variable.

The learning poses various problems in a case such as that of testingfor imperfections in the pipes, in particular due to the fact that heavyengineering is involved, as already indicated.

The applicant first conducted an initial learning by simulation. To thisend it is possible to use the CIVA software developed and marketed bythe Atomic Energy Agency in France. This initial learning allowed theinfluencing parameters to be pinpointed and the construction of aninitial version of the neural network based on virtual imperfections.The cost function was optimised.

The applicant then conducted a second learning combining the resultsobtained from simulation and artificial imperfections, that is to saycreated intentionally on actual pipes. This second learning allowedconstruction of a second version of the neural network, the costfunction of which was also optimised.

The applicant then combined the results obtained with the artificialimperfections, and with a set of imperfections present on actual pipes,these imperfections being known with accuracy from measurementsperformed a posteriori during the production sequence. This third phaseallowed validation of the final version of the neural network. Thisversion has proved itself operationally for production monitoring.However, when implemented in a new or modified installation, it iscurrently necessary to put it through a “calibration” using around tenartificial samples covering the entire range of imperfections to bedealt with. Of course, an optimisation then follows.

FIGS. 11, 12, 14 and 15 were described in connection with sensors P11and P12.

The same principle applies to the group of sensors P1. In this casethere is no image 2 and the network built has less input parameters, asalready indicated. The circuits described for two sensors may be usedfor just one, but without input parameters for the “Image 2” section.

The same principle can also be applied to the two groups of sensors P21and P22, in charge of detecting transversal imperfections, bearing inmind that for this detection the sensors are inclined (for example by±17°) in a plane passing through the axis of the pipe.

It will be understood that, in each case, digital processing takes placeof the type defined by FIG. 11, with the exception of elements 992 to996. This procedure has a global designation, 763, in accordance withFIG. 8 where it is followed by blocks 764 and 766.

A set is in this way obtained as shown by FIG. 18, with:

-   -   for sensor P1, a procedure 763-1, followed by a decision and        alarm phase 764-1;    -   for sensors P11 and P12, a procedure 763-10, followed by a        decision and alarm phase 764-10;    -   for sensors P21 and P22, a procedure 763-20, followed by a        decision and alarm phase 764-20;    -   the three phases 764-1, 764-10 and 764-20 being interpreted        together by the sorting and alarm robot 767.

A variant of FIG. 18, which is not shown, consists of providing only one“decision and alarm” phase, making direct use of the outputs from thethree procedures 763-1, 763-10 and 763-20.

The non-destructive testing, properly so-called, takes place “on thefly”, that is to say as the pipe passes through the test installation.The decision resulting from the processing of the information describedabove can also be taken either as the pipe passes through the testinstallation (with decision-alarm and marking “on the fly”); a variantconsists of taking this decision once the entire length of the pipe hasbeen inspected, or even at a later time (after testing of an entirebatch of pipes, for example), each pipe being referenced/identified(order No. for example). In this case, it is necessary that theinformation obtained is recorded (stored). The recordings can be thesubject of a later analysis by an operator with the authority to take adecision following analysis of the results that have been recorded andprocessed by the neural networks(s).

Of course, given the properties of the neural circuits, it is possibleto combine at least to some extent all the neural networks (contained inprocedures 763-1, 763-10 and 763-20) in a single neural circuit havingall the desired inputs.

The embodiment described makes direct use of neural networks, by way ofexample of expert systems. The invention is not limited to this type ofembodiment. Here the expression “arrangement of the neural circuit type”can cover other non-linear statistical methods with or without neuralcircuits.

Generally speaking, the converter can comprise a maximum amplitude inputin a selector and a corresponding time of flight input. These inputs canprovide sufficient data for the decision on whether a product conformsor not.

The transformer unit can correspond to an unnecessary data removalelement, a pinpointed zones filtering element, a simulator and aninterpretation unit. Reducing the amount of information allows a higherprocessing speed.

The simulator can comprise a theoretical simulation element, a tolerancecalculator and an inverse algorithm.

The output stage can comprise:

-   -   a combiner, arranged to prepare digital inputs for the neural        circuit, from an extract of the images corresponding to a        presumed imperfection zone, properties of the presumed        imperfection in the same zone, coming from the filter, and        contextual data;    -   at least one neural circuit, that receives the inputs from the        combiner;    -   a digital decision and alarm stage, operating on the basis of        the output from the neural circuit, and    -   a sorting and marking robot, arranged to separate and mark pipes        that have been deemed not to conform by the decision and alarm        digital stage.

The system proposed here has been described in the case ofnon-destructive testing in the manufacture of weld-less pipes, a case towhich the invention lends itself particularly well. The same methods canapply in particular to elongated iron and steel products which are notnecessarily tubular.

In the case of welded pipes or other welded products (such as sheets orplates), the system also proves to be capable of determining the limitsof the weld seam, and as a result of locating any imperfections in theweld seam, which it may be necessary to monitor. For their part,imperfections located outside the limits of the weld seam, which maycorrespond to inclusions already present in the base strip (or product),must be considered differently.

APPENDIX

$\begin{matrix}{\mspace{76mu}{{Section}\mspace{14mu} 1}} & \; \\{\mspace{76mu}{Y_{i} = {F\left( {\sum\limits_{j}\;{w_{ij}X_{i}}} \right)}}} & (11) \\{\mspace{76mu}{S_{1} = {F\left( {{\sum\limits_{i = 1}^{N}\;{E_{i}w_{i}}} + w_{0}} \right)}}} & (12) \\{\mspace{76mu}{S = {{\sum\limits_{i = 1}^{k}{S_{i}{w^{\prime}}_{i}}} + {w^{\prime}}_{0}}}} & (13) \\{\mspace{76mu}{D_{p} = {S_{p} - R_{p}}}} & (14) \\{\mspace{76mu}{C_{g} = \frac{\sum\limits_{p = 1}^{p = M}\; D_{p}^{2}}{2\; M}}} & (15) \\{\mspace{76mu}{{Section}\mspace{14mu} 2}} & \; \\{\mspace{76mu}{{pfa} = {{\int_{seuil}^{\infty}{\frac{1}{\sqrt{2\pi}{std}_{b}}{\mathbb{e}}^{- \frac{x - m_{b}^{2}}{2\;{std}_{b}^{2}}}\ {\mathbb{d}x}}} = {Q\left( \frac{{seuil} - m_{b}}{{std}_{b}} \right)}}}} & (21) \\{\mspace{76mu}{{{seuil} = {{{std}_{b}{Q^{- 1}({pfa})}} + m_{b}}}{{seuil} = {threshold}}}} & (22)\end{matrix}$

1. A device forming an operating tool, for non-destructive testing,during or at an end of production, of iron or steel elongated products,the tool configured to extract information on possible defects in theproduct, from feedback signals that are captured, following selectiveexcitation of transmitting ultrasound sensors according to a selectedtime rule, by receiving ultrasound sensors forming an arrangement with aselected geometry, mounted in ultrasound coupling with the product viaan intermediary of a liquid medium, with relative rotation/translationmovement between a pipe and the transducer arrangement, the operatingtool comprising: a converter, capable of selectively isolating a digitalrepresentation of possible echoes in designated time windows, as afunction of the relative rotation/translation movement, therepresentation including amplitude and time of flight of at least oneecho, and of generating a parallelepipedic three dimensional (3D) graph;a transformer unit capable of generating a 3D image of possible defectsin the pipe on the basis of the 3D graph and a database; a filter,capable of determining, in the images, presumed defect zones, andproperties of each presumed defect; and an output stage configured togenerate a product conformity or non-conformity signal, wherein thetransformer unit comprises an unnecessary data removal element, apinpointed zones filtering element, a simulator, and an interpretationunit.
 2. A device according to claim 1, wherein the converter comprisesa maximum amplitude in a selector input and a corresponding time offlight input.
 3. A device according to claim 1, wherein the simulatorcomprises a theoretical simulation element, a tolerance calculator, andan inverse algorithm.
 4. A device according to claim 1, wherein theoutput stage comprises: a combiner, arranged to prepare digital inputsfor a neural circuit, from an extract of the images corresponding to apresumed defect zone, and properties of the presumed defect in a samezone, coming from the filter; at least one arrangement of neural circuittype, that receives inputs from the combiner; a digital decision andalarm stage, operating on the basis of an output from the arrangement ofthe neural circuit type, and a sorting and marking robot, arranged toseparate and mark products that have been deemed not to conform by thedecision and alarm digital stage.
 5. A device according to claim 4,wherein the operating tool comprises two converters respectivelydedicated to two arrangements of ultrasound transducers with a selectedgeometry, mounted in ultrasound coupling roughly according to a mirroredsymmetry of the direction of their respective ultrasound beams, andwherein the combiner is arranged to operate selectively on inner skinechoes or on outer skin echoes or the echoes taking place in a mass ofthe pipe, but at a same time on data relating to one or other of the twotransducer arrangements.
 6. A device according to claim 4, wherein theconverter is arranged to selectively isolate a digital representation ofpossible echo maxima in designated time windows corresponding to innerskin echoes, outer skin echoes, and echoes from a mass of the pipe,respectively, and wherein the combiner is arranged to operateselectively on the inner skin echoes or the outer skin echoes or theechoes occurring in the mass.
 7. A device according to claim 4, whereinthe combiner receives at least one input relating to an amplitudeextremum of the image in the presumed defect zone.
 8. A device accordingto claim 4, wherein the filter is arranged to produce, as properties ofeach presumed defect, its obliquity and its length, while the combinerreceives corresponding inputs of defect obliquity and defect length. 9.A device according to claim 4, wherein the filter, the combiner, theneural circuit, and the digital decision and alarm stage are arranged tooperate iteratively on a series of presumed defect zones, determined bythe filter.
 10. A device according to claim 9, wherein the filter, thecombiner, the neural circuit, and the digital decision and alarm stageare arranged to operate alternately on an inner skin and outer skin ofthe pipe.
 11. A device according to claim 4, wherein the arrangement ofthe neural circuit type comprises: a first neural circuit configured toevaluate a nature of a defect among a number of predefined classes; anda second neural circuit configured to evaluate a severity of a defect.12. A device according to claim 11, wherein the first and second neuralcircuits have inputs that differ by: an input of a number of maxima in avicinity for the first neural circuit, and an input of an echo width forthe second neural circuit.
 13. A device according to claim 11, whereinthe outputs of the first and second neural circuits are combined torefine a prediction.
 14. A device according to claim 1, wherein thetransmission and reception of the ultrasound signals are performed eachtime by a same transducer, for at least part of the arrangement ofsensors.
 15. A non-destructive testing device for pipes during or at anend of production, comprising: an arrangement of ultrasound transducerswith a selected geometry, mounted in ultrasound coupling with a pipe viathe intermediary of a liquid medium, with relative rotation/translationmovement between the pipe and the transducer arrangement; circuits toselectively excite the transducer elements according to a selected timerule and to gather feedback signals captured by the transducer elements;and an operational tool according to claim 1.